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CroneKorkN 2025-05-30 19:20:00 +02:00
parent 4756013c17
commit 4ce4b5eede
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 12 additions and 12 deletions
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24
process_chunks.py
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@ -22,6 +22,17 @@ OVERTONE_FREQ = TARGET_FREQ * 2
NFFT = 32768
SKIP_SECONDS = 10
def detect_event(chunk, samplerate):
freqs, times, Sxx = scipy.signal.spectrogram(chunk, samplerate, nperseg=NFFT)
idx_base = np.where((freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE))[0]
idx_oct = np.where((freqs >= OVERTONE_FREQ - OVERTONE_TOLERANCE) & (freqs <= OVERTONE_FREQ + OVERTONE_TOLERANCE))[0]
if len(idx_base) == 0 or len(idx_oct) == 0:
return False
base_energy = np.mean(Sxx[idx_base])
oct_energy = np.mean(Sxx[idx_oct])
total_energy = np.mean(Sxx, axis=0).max()
return base_energy > THRESHOLD_BASE * total_energy and oct_energy > THRESHOLD_OCT * total_energy
def process_chunk(filename):
input_path = os.path.join(OUTDIR, filename)
print(f"🔍 Verarbeite {input_path}...")
@ -36,22 +47,11 @@ def process_chunk(filename):
padding_before = int(CLIP_PADDING_BEFORE * samplerate)
padding_after = int(CLIP_PADDING_AFTER * samplerate)
def detect_event(chunk):
freqs, times, Sxx = scipy.signal.spectrogram(chunk, samplerate, nperseg=NFFT)
idx_base = np.where((freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE))[0]
idx_oct = np.where((freqs >= OVERTONE_FREQ - OVERTONE_TOLERANCE) & (freqs <= OVERTONE_FREQ + OVERTONE_TOLERANCE))[0]
if len(idx_base) == 0 or len(idx_oct) == 0:
return False
base_energy = np.mean(Sxx[idx_base])
oct_energy = np.mean(Sxx[idx_oct])
total_energy = np.mean(Sxx, axis=0).max()
return base_energy > THRESHOLD_BASE * total_energy and oct_energy > THRESHOLD_OCT * total_energy
i = 0
last_event = -skip_samples
while i + chunk_samples <= len(data):
chunk = data[i:i+chunk_samples]
if i - last_event >= skip_samples and detect_event(chunk):
if i - last_event >= skip_samples and detect_event(chunk, samplerate):
clip_start = max(0, i - padding_before)
clip_end = min(len(data), i + chunk_samples + padding_after)
clip = data[clip_start:clip_end]